Search results for: information recognition

Authors: Kirsten A. Bartels

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There are a number of similarities between survivor literature and Holocaust fiction for children and young adults. The paper explores three facets of the parallels of recognition found specifically between Livia Bitton-Jackson’s memoir of her experience during the Holocaust as an inmate in Auschwitz, I Have Lived a Thousand Years (1999) and Morris Glietzman series of Holocaust fiction. While Bitton-Jackson reflects on her past and Glietzman designs a fictive character, both are judicious with what they are willing to impart, only providing information about their appearance or themselves when it impacts others or when it serves a necessary purpose to the story. Another similarity lies in another critical aspect of many works of Holocaust literature – the idea of being ‘representatively Jewish’. The authors come to this idea from different angles, perhaps best explained as the difference between showing and telling, for Bitton-Jackson provides personal details, and Gleitzman constructed Felix arguably with this idea in mind. Interwoven through their journeys is a shift in perspectives on being recognized -- from wanting to be seen as individuals to being seen as Jew. With this, being Jewish takes on different meaning, both youths struggle with being labeled as something they do not truly understand, and may have not truly identified with, from a label, to a death warrant. With survivor literature viewed as the most credible and worthwhile type of Holocaust literature and Holocaust fiction is often seen as the least (with children’s and young-adult being the lowest form) the similarities in approaches to telling the stories may go overlooked or be undervalued. This paper serves as an exploration in the some of parallel messages shared between the two.

Keywords: holocaust fiction, Holocaust literature, representatively Jewish, survivor literature

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Ming-Yen Chang, Sheng-Hung Ke

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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.

Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Mohammadamin Abbasnejad

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The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition.

Keywords: Gaussian Conditional Random Field, ADMM, convergence, gradient descent

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Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Wale Agbaje

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The aim of this research study is to assess the impact of financial statement fraud on profitability of some selected Nigerian manufacturing firms covering (2002-2016). The specific objectives focused on to ascertain the effect of incorrect asset valuation on return on assets (ROA) and to ascertain the relationship between improper expense recognition and return on assets (ROA). To achieve these objectives, descriptive research design was used for the study while secondary data were collected from the financial reports of the selected firms and website of security and exchange commission. The analysis of covariance (ANCOVA) was used and STATA II econometric method was used in the analysis of the data. Altman model and operating expenses ratio was adopted in the analysis of the financial reports to create a dummy variable for the selected firms from 2002-2016 and validation of the parameters were ascertained using various statistical techniques such as t-test, co-efficient of determination (R2), F-statistics and Wald chi-square. Two hypotheses were formulated and tested using the t-statistics at 5% level of significance. The findings of the analysis revealed that there is a significant relationship between financial statement fraud and profitability in Nigerian manufacturing industry. It was revealed that incorrect assets valuation has a significant positive relationship and so also is the improper expense recognition on return on assets (ROA) which serves as a proxy for profitability. The implication of this is that distortion of asset valuation and expense recognition leads to decreasing profit in the long run in the manufacturing industry. The study therefore recommended that pragmatic policy options need to be taken in the manufacturing industry to effectively manage incorrect asset valuation and improper expense recognition in order to enhance manufacturing industry performance in the country and also stemming of financial statement fraud should be adequately inculcated into the internal control system of manufacturing firms for the effective running of the manufacturing industry in Nigeria.

Keywords: Althman's Model, improper expense recognition, incorrect asset valuation, return on assets

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Authors: Hee-Choul Kwon, Hyungjin Cho, Heeyong Kwon

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Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones.

Keywords: high speed rotation operation, image processing, image rotation, pattern recognition, transformation matrix

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Prakash, Vikash Kumar, Vinay Bansal, L. N. Das

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Dual biometrics is a subpart of multimodal biometrics, which refers to the use of a variety of modalities to identify and authenticate persons rather than just one. We limit the risks of mistakes by mixing several modals, and hackers have a tiny possibility of collecting information. Our goal is to collect the precise characteristics of iris and palmprint, produce a fusion of both methodologies, and ensure that authentication is only successful when the biometrics match a particular user. After combining different modalities, we created an effective strategy with a mean DI and EER of 2.41 and 5.21, respectively. A biometric system has been proposed.

Keywords: multimodal, fusion, palmprint, Iris, EER, DI

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Authors: H. Gonzalez-Rivera, J. L. Palmeros-Torres

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This paper describes the application of traditional computer vision techniques as a procedure for automatic measurement of the secondary dendrite arm spacing (SDAS) from microscopic images. The algorithm is capable of finding the lineal or curve-shaped secondary column of the main microstructure, measuring its length size in a micro-meter and counting the number of spaces between dendrites. The automatic characterization was compared with a set of 1728 manually characterized images, leading to an accuracy of −0.27 µm for the length size determination and a precision of ± 2.78 counts for dendrite spacing counting, also reducing the characterization time from 7 hours to 2 minutes.

Keywords: dendrite arm spacing, microstructure inspection, pattern recognition, polynomial regression

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Authors: Laura Victoria Vigoya Morales, David Rolando Suarez Mora

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The problem of detecting enterococcus faecalis is receiving considerable attention with the new cases of beachgoers infected with the bacteria, which can be found in fecal matter. The process detection of this kind of bacteria would be taking a long time, which waste time and money as a result of closing recreation place, like beach or pools. Hence, new methods for automating the process of detecting and recognition of this bacteria has become in a challenge. This article describes a novel approach to detect the enterococcus faecalis bacteria in water by using an octave algorithm, which embody a network neural. This document shows result of performance, quality and integrity of the algorithm.

Keywords: Enterococcus faecalis, image treatment, octave and network neuronal

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Maria Barriga

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The article focuses on the analysis of three images of the last five years that represent different visions of social groups in the context of the so call “Conflicto Mapuche” in la Araucanía, Chile. Using a multimodal social semiotic approach, we analyze the meaning making of these images and the social groups strategies to achieve visibility and recognition in political contexts. We explore the making and appropriation of symbols and concepts and analyze the different strategies that groups use to built hegemonic views. Among these strategies, we compare the use of digital technologies in design these images and the influence of Chilean Estate's vision on the Mapuche political conflict. Finally, we propose visual strategies to improve basic conditions for dialogue and recognition among these groups.

Keywords: visual culture, power, conflict, indigenous people

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Authors: Zsuzsanna Schnell, Francesca Ervas

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Background: the experimental pragmatic study confirms a basic tenet in the Relevance theoretical views in language philosophy. It draws up a developmental trajectory of the maxims, revealing the cognitive difficulty of their interpretation, their relative place to each other, and the order they may follow in development. A central claim of the present research is that social-cognitive skills play a significant role in inferential meaning construction. Children passing the False Belief Test are significantly more successful in tasks measuring the recognition of the infringement of conversational maxims. Aims and method: Preschoolers’ conversational skills and pragmatic competence is examined in view of their mentalization skills. In doing so it use a measure of linguistic tasks, containing 5 short scenarios for each Gricean maxim. it measure preschoolers’ ToM performance with a first- and a second order ToM task and compare participants’ ability to recognize the infringement of the Gricean maxims in view of their social cognitive skills. Results: Findings suggest that Theory of Mind has a predictive force of 75% concerning the ability to follow Gricean maxims efficiently. ToM proved to be a significant factor in predicting the group’s performance and success rates in 3 out of 4 maxim infringement recognition tasks: in the Quantity, Relevance and Manner conditions, but not in the Quality trial. Conclusions: the results confirm that children’s communicative competence in social contexts requires the development of higher-order social-cognitive reasoning, and reveal the cognitive effort needed for the recognition of the infringement of each maxim, yielding a continuum of their cognitive difficulty and trajectory of development.

Keywords: maxim infringement recognition, social cognition, Gricean maxims, developmental pragmatics

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Authors: Anthony Albanese

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While much of the literature concerning Max Weber’s concept of charisma has addressed the importance of the follower’s recognition of and devotion to the charismatic leader, very little has been said about the processes that lead to the development of follower charisma. This article examines this largely overlooked aspect of the concept, as doing so (1) exacts the dynamics behind charisma’s transferability by moving beyond follower-centric models that focus on the recognition of the leader and toward one that emphasizes the follower’s generation and exhibition of charisma, (2) bridges a crucial gap between the rather wanting “losers of modernization” thesis and the social actor’s proclivity to produce stories and self-cast in said stories, (3) presents authoritarian dispositions as a reaction to the weakening effects everydayness have on charisma, and (4) complicates Weber’s formulation by reassessing the role of continually demonstrable mastery. To illustrate these dynamics, one should turn to the January 6th Capitol attack in the United States.

Keywords: max weber, extremism, right-wing populism, charisma

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Authors: Daseul Lee, Jeong Jun Lee, Ok-Sang Jung

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Self-assembly of a series of ZnX2 (X- = Cl-, Br-, and I-) with 2,3-bis(4’-nicotinamidephenoxy)naphthalene (L) as a new bidentate pyridyl-donor ligand yields systematic metallacyclodimeric unit, [ZnX2L]2. The supramolecule constitutes a characteristically stacked forming both 1D suprachannels and cages. Weak C-H⋯π and inter-digitated π⋯π interactions are main driving forces in the formation of both suprachannels and cages. The slightly different features between the suprachannel and cage have been investigated by 1H NMR and TG analysis, which solvent quantitatively exchange within only suprachannels. Photo-unstable CH2I2 molecules are stabilized via capturing within suprachannels, which is monitored by UV-Vis spectroscopy. Furthermore, the photoluminescence intensity, from the chromophore naphthyl moiety of [ZnCl2L]2, gradually decreases with the addition of CH2I2. And washing off the CH2I2 by dichloromethane returned the PL intensity back to its approximately original signal.

Keywords: metallacyclodimer, suprachannel, π⋯π interaction, molecular recognition

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Authors: Ajayi Oluwasola Felix

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Management information system (MIS) provides information for the managerial activities in an organization. The main purpose of this research is, MIS provides accurate and timely information necessary to facilitate the decision-making process and enable the organizations planning control and operational functions to be carried out effectively. Management information system (MIS) is basically concerned with processing data into information and is then communicated to the various departments in an organization for appropriate decision-making. MIS is a subset of the overall planning and control activities covering the application of humans technologies, and procedures of the organization. The information system is the mechanism to ensure that information is available to the managers in the form they want it and when they need it.

Keywords: Management Information Systems (MIS), information technology, decision-making, MIS in Organizations

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Authors: Bipasha Sen, Aditya Agarwal

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Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4x and 7.4x respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Keywords: convolutional neural networks, language compatibility, low resource languages, multilingual automatic speech recognition

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Authors: Dev Pratap Singh, Harshika Hasija, Ashwini S.

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computers using hand gestures and voice commands. The system leverages advanced computer vision techniques using the Media Pipe framework and OpenCV to detect and interpret real-time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the speech recognition library allows for seamless execution of tasks like web searches, location navigation, and gesture control in the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks, natural language processing, voice assistant

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Authors: Ashis Pradhan, Mohan P. Pradhan

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Pattern Matching is useful for recognizing character in a digital image. OCR is one such technique which reads character from a digital image and recognizes them. Line segmentation is initially used for identifying character in an image and later refined by morphological operations like binarization, erosion, thinning, etc. The work discusses a recognition technique that defines a set of morphological operators based on its orientation in a character. These operators are further categorized into groups having similar shape but different orientation for efficient utilization of memory. Finally the characters are recognized in accordance with the occurrence of frequency in hierarchy of significant pattern of those morphological operators and by comparing them with the existing database of each character.

Keywords: binary image, morphological patterns, frequency count, priority, reduction data set and recognition

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Authors: Adam Johnbull

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The paper discusses ICT in Education as a quest for quality learning in the 21st century. Education is the key that unlock the door to development, without adequate education of the citizenry, the development of a nation becomes a sham. Information Communication Technologies (ICTs) has revolutionized the way people work today and are now transforming education systems. As a result, if schools train children in yesterday’s skills and technologies they may not be effective and fit in tomorrow’s world. This is a sufficient reason for ICT’s to win global recognition and attention and thus ensure desire quality in our school system. Thus, the purpose of the paper is to discuss amongst others, what is ICT. The roles of ICT’s in education, limitation and key challenges of integrating ICT to education in the enhancement of student learning and experiences in other to encourage policy makers, school administrators and teachers pay the required attention to integrate this technology in the education system. The paper concludes that regardless of all the limitation characterizing it. ICT benefit education system to provide quality education in the 21st century.

Keywords: ICTs, quest, information, global, sham, century

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Authors: Olga Hernán-Vega, Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio, Ignacio Bravo

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Non-Cooperative Target Identification has become a key research domain in the Defense industry since it provides the ability to recognize targets at long distance and under any weather condition. High Resolution Range Profiles, one-dimensional radar images where the reflectivity of a target is projected onto the radar line of sight, are widely used for identification of flying targets. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition to a matrix of range profiles is presented. Target Identification based on one-dimensional radar images compares a collection of profiles of a given target, namely test set, with the profiles included in a pre-loaded database, namely training set. The classification is improved by using Singular Value Decomposition since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information such as noise. Singular Value Decomposition permits to define a signal subspace which contain the highest percentage of the energy, and a noise subspace which will be discarded. This way, only the valuable information of each target is used in the recognition process. The identification algorithm is based on finding the target that minimizes the angle between subspaces and takes place in a transformed domain. Two metrics, F1 and F2, based on Singular Value Decomposition are accomplished in the identification process. In the case of F2, the angle is weighted, since the top vectors set the importance in the contribution to the formation of a target signal, on the contrary F1 simply shows the evolution of the unweighted angle. In order to have a wide database or radar signatures and evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, since measured profiles suffer from noise, clutter and other unwanted information and simulated profiles don't. In this case, the test and training samples have similar nature and usually a similar high signal-to-noise ratio, so as to assess the feasibility of the approach, the addition of noise has been considered before the creation of the test set. The identification results applying the unweighted and weighted metrics are analysed for demonstrating which algorithm provides the best robustness against noise in an actual possible scenario. So as to confirm the validity of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results. Considering the dissimilarities between the test and training sets when noise is added, the recognition performance has been improved when weighting is applied. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.

Keywords: HRRP, NCTI, simulated/synthetic database, SVD

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Authors: Eduardo Kanagushiku Pereira, Frank Gregory Cavalcante da Silva, Barbara Soares Gonçalves, Ana Lúcia Bergamasco Galastri, Ronei Luciano Mamoni

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The inflammatory response initiates after the recognition of pathogens by receptors expressed by innate immune cells. Among these receptors, the NLRP3 was associated with the recognition of pathogenic fungi in experimental models. NLRP3 operates forming a multiproteic complex called inflammasome, which actives caspase-1, responsible for the production of the inflammatory cytokines IL-1beta and IL-18. In this study, we aimed to investigate the involvement of NLRP3 in the inflammatory response elicited in macrophages against Paracoccidioides brasiliensis (Pb), the etiologic agent of PCM. Macrophages were differentiated from THP-1 cells by treatment with phorbol-myristate-acetate. Following differentiation, macrophages were stimulated by Pb yeast cells for 24 hours, after previous treatment with specific NLRP3 (3,4-methylenedioxy-beta-nitrostyrene) and/or caspase-1 (VX-765) inhibitors, or specific inhibitors of pathways involved in NLRP3 activation such as: Reactive Oxigen Species (ROS) production (N-Acetyl-L-cysteine), K+ efflux (Glibenclamide) or phagossome acidification (Bafilomycin). Quantification of IL-1beta and IL-18 in supernatants was performed by ELISA. Our results showed that the production of IL-1beta and IL-18 by THP-1-derived-macrophages stimulated with Pb yeast cells was dependent on NLRP3 and caspase-1 activation, once the presence of their specific inhibitors diminished the production of these cytokines. Furthermore, we found that the major pathways involved in NLRP3 activation, after Pb recognition, were dependent on ROS production and K+ efflux. In conclusion, our results showed that NLRP3 participates in the recognition of Pb yeast cells by macrophages, leading to the activation of the NLRP3-inflammasome and production of IL-1beta and IL-18. Together, these cytokines can induce an inflammatory response against P. brasiliensis, essential for the establishment of the initial inflammatory response and for the development of the subsequent acquired immune response.

Keywords: inflammation, IL-1beta, IL-18, NLRP3, Paracoccidioidomycosis

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: K. Prabhu, K. Dinesh, M. Ranjani, M. Suhitha

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During the tough times of covid, those people who were hospitalized found it difficult to always convey what they wanted to or needed to the attendee. Sometimes the attendees might also not be there. In that case, the patients can use simple hand gestures to control electrical appliances (like its set it for a zero watts bulb)and three other gestures for voice note intimation. In this AI-based hand recognition project, NodeMCU is used for the control action of the relay, and it is connected to the firebase for storing the value in the cloud and is interfaced with the python code via raspberry pi. For three hand gestures, a voice clip is added for intimation to the attendee. This is done with the help of Google’s text to speech and the inbuilt audio file option in the raspberry pi 4. All the five gestures will be detected when shown with their hands via the webcam, which is placed for gesture detection. The personal computer is used for displaying the gestures and for running the code in the raspberry pi imager.

Keywords: nodeMCU, AI technology, gesture, patient

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Authors: Zahra Alipour, Amirreza Moheb Afzali

Abstract:

In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience.

Keywords: YOLOv8, mediapipe, finger tracking, joint estimation, human-computer interaction (HCI)

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Authors: Maha Saliba Foster

Abstract:

Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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